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[Python/Data Analysis] Numpay - Array Tranposition 사용하기 - Day 6 본문
[Python/Data Analysis] Numpay - Array Tranposition 사용하기 - Day 6
shun10114 2017. 6. 8. 09:10# [Python/Data Analysis] Numpay - Array Tranposition 사용하기 - Day 6
import numpy as np
arr = np.arange(50).reshape((10,5))
arr
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49]])
arr.T
array([[ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45],
[ 1, 6, 11, 16, 21, 26, 31, 36, 41, 46],
[ 2, 7, 12, 17, 22, 27, 32, 37, 42, 47],
[ 3, 8, 13, 18, 23, 28, 33, 38, 43, 48],
[ 4, 9, 14, 19, 24, 29, 34, 39, 44, 49]])
np.dot(arr.T,arr)
array([[7125, 7350, 7575, 7800, 8025],
[7350, 7585, 7820, 8055, 8290],
[7575, 7820, 8065, 8310, 8555],
[7800, 8055, 8310, 8565, 8820],
[8025, 8290, 8555, 8820, 9085]])
arr3d = np.arange(50).reshape((5,5,2))
arr3d
array([[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7],
[ 8, 9]],
[[10, 11],
[12, 13],
[14, 15],
[16, 17],
[18, 19]],
[[20, 21],
[22, 23],
[24, 25],
[26, 27],
[28, 29]],
[[30, 31],
[32, 33],
[34, 35],
[36, 37],
[38, 39]],
[[40, 41],
[42, 43],
[44, 45],
[46, 47],
[48, 49]]])
arr3d.transpose((1,0,2))
array([[[ 0, 1],
[10, 11],
[20, 21],
[30, 31],
[40, 41]],
[[ 2, 3],
[12, 13],
[22, 23],
[32, 33],
[42, 43]],
[[ 4, 5],
[14, 15],
[24, 25],
[34, 35],
[44, 45]],
[[ 6, 7],
[16, 17],
[26, 27],
[36, 37],
[46, 47]],
[[ 8, 9],
[18, 19],
[28, 29],
[38, 39],
[48, 49]]])
arr = np.array([[1,2,3]])
arr
array([[1, 2, 3]])
arr.swapaxes(0,1)
array([[1],
[2],
[3]])
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